{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "nZm3k_JwAxFK",
        "colab_type": "code",
        "outputId": "7cb5a5c7-6a77-4c08-a6b4-535c994d1894",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        }
      },
      "cell_type": "code",
      "source": [
        "!pip3 install torch torchvision"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.0.0)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.2.1)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (5.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5oX6ISEIaA5k",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "PKcBS6xfBK1Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch.nn.functional as F\n",
        "from torch import nn\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hC4ZVkTRB79P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([transforms.Resize((28,28)),\n",
        "                               transforms.ToTensor(),\n",
        "                               transforms.Normalize((0.5,), (0.5,))\n",
        "                               ])\n",
        "training_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "validation_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
        "\n",
        "training_loader = torch.utils.data.DataLoader(training_dataset, batch_size=100, shuffle=True)\n",
        "validation_loader = torch.utils.data.DataLoader(validation_dataset, batch_size = 100, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jCqX-QWODId3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def im_convert(tensor):\n",
        "  image = tensor.clone().detach().numpy()\n",
        "  image = image.transpose(1, 2, 0)\n",
        "  image = image * np.array((0.5, 0.5, 0.5)) + np.array((0.5, 0.5, 0.5))\n",
        "  image = image.clip(0, 1)\n",
        "  return image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8bUYaxqxGFET",
        "colab_type": "code",
        "outputId": "4946bab5-02fb-4cb1-987b-67da8126f500",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "cell_type": "code",
      "source": [
        "dataiter = iter(training_loader)\n",
        "images, labels = dataiter.next()\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "  ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])\n",
        "  plt.imshow(im_convert(images[idx]))\n",
        "  ax.set_title([labels[idx].item()])\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb4164ccf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ELDFsBKeHqJy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Classifier(nn.Module):\n",
        "    \n",
        "    def __init__(self, D_in, H1, H2, D_out):\n",
        "        super().__init__()\n",
        "        self.linear1 = nn.Linear(D_in, H1)\n",
        "        self.linear2 = nn.Linear(H1, H2)\n",
        "        self.linear3 = nn.Linear(H2, D_out)\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.linear1(x))  \n",
        "        x = F.relu(self.linear2(x))\n",
        "        x = self.linear3(x)\n",
        "        return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cOa--7ggN8Nu",
        "colab_type": "code",
        "outputId": "4b545dc9-55b2-460e-b1b2-a2faf6ce96a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "cell_type": "code",
      "source": [
        "model = Classifier(784, 125, 65, 10)\n",
        "model"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Classifier(\n",
              "  (linear1): Linear(in_features=784, out_features=125, bias=True)\n",
              "  (linear2): Linear(in_features=125, out_features=65, bias=True)\n",
              "  (linear3): Linear(in_features=65, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "metadata": {
        "id": "w6wxPBg_Od3t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.0001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zsd3HPylP9UE",
        "colab_type": "code",
        "outputId": "af979d67-6107-4d46-879c-6cd7745751df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        }
      },
      "cell_type": "code",
      "source": [
        "epochs = 15\n",
        "running_loss_history = []\n",
        "running_corrects_history = []\n",
        "val_running_loss_history = []\n",
        "val_running_corrects_history = []\n",
        "\n",
        "for e in range(epochs):\n",
        "  \n",
        "  running_loss = 0.0\n",
        "  running_corrects = 0.0\n",
        "  val_running_loss = 0.0\n",
        "  val_running_corrects = 0.0\n",
        "  \n",
        "  for inputs, labels in training_loader:\n",
        "    inputs = inputs.view(inputs.shape[0], -1)\n",
        "    outputs = model(inputs)\n",
        "    loss = criterion(outputs, labels)\n",
        "    \n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    \n",
        "    _, preds = torch.max(outputs, 1)\n",
        "    running_loss += loss.item()\n",
        "    running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "  else:\n",
        "    with torch.no_grad():\n",
        "      for val_inputs, val_labels in validation_loader:\n",
        "        val_inputs = val_inputs.view(val_inputs.shape[0], -1)\n",
        "        val_outputs = model(val_inputs)\n",
        "        val_loss = criterion(val_outputs, val_labels)\n",
        "        \n",
        "        _, val_preds = torch.max(val_outputs, 1)\n",
        "        val_running_loss += val_loss.item()\n",
        "        val_running_corrects += torch.sum(val_preds == val_labels.data)\n",
        "      \n",
        "    epoch_loss = running_loss/len(training_loader)\n",
        "    epoch_acc = running_corrects.float()/ len(training_loader)\n",
        "    running_loss_history.append(epoch_loss)\n",
        "    running_corrects_history.append(epoch_acc)\n",
        "    \n",
        "    val_epoch_loss = val_running_loss/len(validation_loader)\n",
        "    val_epoch_acc = val_running_corrects.float()/ len(validation_loader)\n",
        "    val_running_loss_history.append(val_epoch_loss)\n",
        "    val_running_corrects_history.append(val_epoch_acc)\n",
        "    print('epoch :', (e+1))\n",
        "    print('training loss: {:.4f}, acc {:.4f} '.format(epoch_loss, epoch_acc.item()))\n",
        "    print('validation loss: {:.4f}, validation acc {:.4f} '.format(val_epoch_loss, val_epoch_acc.item()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch : 1\n",
            "training loss: 0.9646, acc 75.6217 \n",
            "validation loss: 0.4256, validation acc 88.7200 \n",
            "epoch : 2\n",
            "training loss: 0.3802, acc 89.3400 \n",
            "validation loss: 0.3211, validation acc 91.0300 \n",
            "epoch : 3\n",
            "training loss: 0.3147, acc 90.8967 \n",
            "validation loss: 0.2840, validation acc 91.9500 \n",
            "epoch : 4\n",
            "training loss: 0.2816, acc 91.8800 \n",
            "validation loss: 0.2595, validation acc 92.6100 \n",
            "epoch : 5\n",
            "training loss: 0.2559, acc 92.6200 \n",
            "validation loss: 0.2363, validation acc 93.1900 \n",
            "epoch : 6\n",
            "training loss: 0.2356, acc 93.2700 \n",
            "validation loss: 0.2163, validation acc 93.6900 \n",
            "epoch : 7\n",
            "training loss: 0.2168, acc 93.8567 \n",
            "validation loss: 0.2057, validation acc 94.0400 \n",
            "epoch : 8\n",
            "training loss: 0.2024, acc 94.1633 \n",
            "validation loss: 0.1905, validation acc 94.4200 \n",
            "epoch : 9\n",
            "training loss: 0.1882, acc 94.6333 \n",
            "validation loss: 0.1850, validation acc 94.6300 \n",
            "epoch : 10\n",
            "training loss: 0.1767, acc 94.8950 \n",
            "validation loss: 0.1737, validation acc 94.7900 \n",
            "epoch : 11\n",
            "training loss: 0.1660, acc 95.2000 \n",
            "validation loss: 0.1700, validation acc 94.9800 \n",
            "epoch : 12\n",
            "training loss: 0.1563, acc 95.4600 \n",
            "validation loss: 0.1544, validation acc 95.4100 \n",
            "epoch : 13\n",
            "training loss: 0.1480, acc 95.7183 \n",
            "validation loss: 0.1497, validation acc 95.3900 \n",
            "epoch : 14\n",
            "training loss: 0.1404, acc 95.9483 \n",
            "validation loss: 0.1467, validation acc 95.4600 \n",
            "epoch : 15\n",
            "training loss: 0.1336, acc 96.1500 \n",
            "validation loss: 0.1367, validation acc 95.7500 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qq4-LouHVQwp",
        "colab_type": "code",
        "outputId": "5bd1f8dd-1f2c-4355-85f9-47d4206f2850",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(running_loss_history, label='training loss')\n",
        "plt.plot(val_running_loss_history, label='validation loss')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fcb4140be80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+QkWFZ9Tnl8yLA9Abjh3zPdNJPrRhPNTO/KJ25he1M7PGdU54pOSISU7BYJC7776b3//+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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb413785f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "R1A-fOideMnx",
        "colab_type": "code",
        "outputId": "de77cb7f-fa4d-4ad9-fec0-6baaf274cb37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(running_corrects_history, label='training accuracy')\n",
        "plt.plot(val_running_corrects_history, label='validation accuracy')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fcb456383c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BumZWrGtm7fb23i30ivNtfGpKGTPGlDCxqhCrJkqJyCCi8JVD1h0Ls65tAzWt\na/nYt47tnTvXL3aaHcz0TmNi0VgmeMbhdRQf0phqJBrn460+VqxLXk5ubk+ODxuAMcMKmDG2mBlj\nShjmdWnsVkQGLYWvfGKxeIzNHdt6erZr2di+hVjPrT9mo5mJnnFM8IxlQtFYRriHHdSY7a7aO7v5\nYH3ycvJHG1vpjiTP5bCZmD2xlBljk+O3bqdmJotIdlD4Sr8SiQT1wcaenu1a1vo20BVLbuJuwMAI\n9zAmFiUDd3RBNVbToS2rmEgk2NLQyYp1zaxY38zGup2Xo8uKnMwYU8yMsSWMG16gXX9EJCspfKVP\nvq625Lhtz9hte3hnAJY6SphddBgTPWMZ5xmD6wBXj9qfru4o/13bxIp1LXywvpm2zuQ6zCajgUkj\nPcwYU8z0sSWUFx36uUREMk3hK0Byyca1vvWsbKmhpnUtDcHG3ufcljyOKJvJhJ7LycUOT0rOGeiK\nsHxNE+993MTqzT4i0eQyjnkOC3OnljNjbAlTqotw2vXXVERyi36rDWEd4U4+aqnhw+bVrG79mO6e\nXX+sJitTiicy0TOWCUXjqHSVp2zyUrAryvvrmli2upGPNrb2LnZRXZHPlGoPM8aWMLoiX2smi0hO\nU/gOIYlEgrpAAyubV/Nhyyo2tm/p3fnH6yhmWslkppVMYnRBNWZj6v5qhLqjrFjXzDs1jXy4obV3\no4Kq0jxmTypl9sRSpowv0z63IjJkKHxzXCweY23bhmTgNq+iuasVSE6UGl0wsidwJ1Pm9Kb01pzu\ncIwV65t5Z3UjH2xo6b2kPNzrYvbEUmZPKtP4rYgMWQrfHBSIBHsuJ69iVcsaumLJe2HtJhuzSqcz\nrXgSU4onkmd1pfS84UiMDze0sGx1IyvWNxOOJAO3otjZG7jDSlJ7ThGRbKTwzRENgUY+bEn2bje0\nbyaeSAZfsd3DpyoOZ1rJJMYVjk7p5WSASDTGyg2tvFPTyH/XNdMdTt6DW+ZxMHtSGXMmlmrBCxGR\nPSh8s1QsHmND+yY+aF7FyubVNIaageTl5Or8Eb2XkytcZSkPvmgszkcbW1m2upH31zUR6k4GbkmB\nnZMPG86cSaWMKM1T4IqI7IPCN4sEIyFWtX7cczn5Y4LR5D60VpOVGd6pTCuexNSSSYe0OcG+RGNx\najb7WLa6keVrmno3LSjOt3H8jGHMnlRKdblbgSsicgAUvoNcfWcTr219hw+bVrGufWPv5eRCWwGH\nl81kWskkxheOwXKIq0r1JRaPU7OljXd6ArczFAF6NpyfXsHsiaWMrsxX4IqIfEIK30EoFO1iad17\n/Kd2KbWB+t7jVe7hTC+ZzNRhAHn5AAAemUlEQVSSyQzPq0hL6MXjCdZsbWNZTSPvfdxIRzAZuAUu\nKycfPpzZE0sZO7wAowJXROSgKXwHkdrOel7f/hbL6t+jOxbGbDBxeOU0JrjHM6VkIoW2grSduzMU\nYcl/t/Pq8m29Szu6nRZOnDWMOZNKGTe8UAtfiIikiMI3w2LxGB80r+L1bW+ypm09kLyk/OmRJ3F0\n5RxGD6tI6+ITDb4gL7+zlX9/WEc4EsduNXHcjErmTCplQlUhJqM2LhARSTWFb4b4wx28WbuMN7a/\nTVt3OwDjPWM5fvhcphVPwmRM3+bviUSCddvbeXHZVv67pokEyYlTpxw7guNmVOKw6a+FiEg66bfs\nAEokEmz0b+H1bW+yvPEDYokYNpOV44bN5bjhR1HhKkvr+WPxOMvXNPPisi1sqPUDUF3u5rQ5VRwx\n0aterojIAFH4DoBwLMJ7De/z2vY32dqxHYByZynHDZ/LnPLDcJjtaT1/qDvKvz+o4+V3t9Lc3oUB\nmDm2hNPmjGD8iELNVhYRGWAK3zRqDrXyxva3eKv2HQLRIAYMzPBO5fhhcxnvGZP20Gv1d/HKe9t4\n7f1aQt1RrGYjJ84axqmzR2hdZRGRDFL4plg8EaemdS2vbXuTj1pqSJAgz+LitJEnccywT1FkT81e\nuPuzub6DF9/ZwjurG4nFE+S7rHxmzihOmDUMt9Oa9vOLiMj+KXxTJBgJ8Xb9u7y+7U2aQi0AVOdX\ncfzwucwqnY4lxWsq7ymeSPDh+hZeXLaFmi1tAFSWuDht9giOnFKGxZy+CVwiIvLJKHwP0fbOOl7b\n9ibv1C8nHI9gNpo5svwIjht+FCPzR6T9/JFojDdX1vPSO1upawkCMLnaw2lzqpg6qkjjuSIig5DC\n9yDE4jHeb1rJa9veZH37RiC5e9Cxw47iqIrZKd+qry/+YJh/LU8uitERjGAyGpg7tZxPzx5BVZk7\n7ecXEZGDp/D9BNq7/fy7din/2f427eHkwheTisZz/PC5TCmeiNGQ/lt16loCvPTOVt5cWU8kGsdp\nM3PGkSM5+fDheNy2tJ9fREQOncL3AHRFu3lizbO82/A+8UQch9nOicOP4djhR1Hm9Kb9/IlEgo+3\ntPHisi2sWJ8cTy4psPPp2SM4ZnoFdqt+jCIi2US/tfsRiUX43YePssa3jkpXOccNn8vsslnYzenv\nZUZjcZYs38ZTr6xhc0Oypz1mWD6nza7isPFerbUsIpKlFL77EYvHePCjx1jjW8eMkilcOnV+Wpd9\n3NXqzT4efmF1clEMAxw+wctpc6oYOyx9myuIiMjAUPjuQzwR59FVf+bD5tVM9Izjq1MvHJDgjcbi\nPPv6Bv65dAtGo4HPHj2KY6aVU1roSPu5RURkYCh8+5BIJPhTzTO817iCMQXVXDb94rTfpwvJyVT3\nP7+KzQ0dlHocXPa5KXxqxrC07mokIiIDT+G7h0QiwdPr/sqbdcsY4R7G5TO+is2U3lWhEokEr71f\ny58XryUcjXPs9Aq+fMo4TaQSEclR+u2+h79vfJl/bf035a4yrpzxdRzm9F7u9QfDPPJCDe+va8Zl\nN/P1z07miImlaT2niIhklsJ3F69seY1/bHqFEnsRV838etoXy1i5sYUH/7aa9kCYSSM9XHrmJIry\n07vDkYiIZJ7Ct8cb29/m2XV/p9BWwHdmXUahLX2ziiPRGE8t2cDL727FZDRw3olj+fScERi1FKSI\nyJCg8AWW1S/niY+fJc/i4qqZ36DYUZS2c21r6uT+5z9iW1OAimInl31uCiPLtRykiMhQMuTDd0XT\nSv6w+knsZjtXzfwG5a70jLcmEgkWv7eNJ/+1nmgszomzhnHeSWOxWbTbkIjIUNNv+MbjcW644QbW\nrl2LxWLhxhtvxOl0cu211xKLxfB6vfziF7/Aas2+fWJXt67hoZV/xGw0c8WMrzHcXZmW87QHwjz0\n99V8uKGFPIeFr50xlZnjStJyLhERGfz6Dd/FixfT0dHBn//8Z7Zs2cItt9xCUVER8+bN4/TTT+fO\nO+/kqaeeYt68eQNRb8qsa9vI7z54FAwGvjXtEkYVjEzLeVasa+ahF1bTEYwwdVQRl545iYI8bYAg\nIjKU9bsNz6ZNm5g+fToAVVVV1NbWsnTpUk4++WQATjzxRN566630VpliW/zbuG/Fw8QSMb4+dT4T\nisam/BzhSIzHXvqYu5/6gFB3jC+fMo7/OW+GgldERPoP3/Hjx/Pvf/+bWCzGhg0b2Lp1K9u3b++9\nzFxcXExTU1PaC02V2s567l3xAN2xbi6ZfAHTSian/BxbGjr4ySPv8Ory7Qzzurj+4iM49QjNZhYR\nkaR+Lzsff/zxLF++nAsvvJAJEyYwevRo1qxZ0/t8IpHo9yQejxOzOfUTi7zeTzZLuL6zid+8+SCB\nSJBvzb6Ik0bPTWk98XiC515fz/97YTXRWJzPHTuai8+cfMiTqj5pO7OV2plb1M7conam1gHNdv7u\nd7/b+/iUU06hrKyMrq4u7HY7DQ0NlJbuf4awzxc8tCr74PW6P9Gax76uNu5cfh++rna+NO4sprmn\npXTNZF9HNw/8bRWrN/vId1n52hmTmD6mGH/bobX9k7YzW6mduUXtzC1q58G/3770e9m5pqaGH/zg\nBwC8/vrrTJ48mblz5/Liiy8C8NJLL3HsscemqNT06Ah38qv3f09rl4/PjjqNE0cck9L3f+/jJq5/\ncCmrN/uYMaaYm742h+ljilN6DhERyR399nzHjx9PIpHgS1/6Ejabjdtvvx2TycSCBQt44oknqKys\n5Atf+MJA1HpQgpEgv3r/9zQEmzi16gQ+U31Syt67Kxzlz4vX8vqKOixmIxd9ejwnzBqGQWO7IiKy\nH/2Gr9Fo5Oc///lexx9++OG0FJRKXdEufrPiIbZ31nHssKP4/JjTUxaMG+v83P/8RzT4QowozeOb\nZ02hsiS9a0GLiEhuyNkVrsKxCL/74FE2+rcwp/wwzhv/+ZQEbzye4B9LN/OXNzYSiyc4bc4Izj5u\nDBZzv1fwRUREgBwN31g8xoMr/8CatvXM8E5l/sRzMRoOPRxb2rv4/d9WsWZrGwV5Vr7+2clMqU7f\nOtAiIpKbci5844k4j676MytbaphUNJ6vTpmHyXjotzm9W9PII/+oIdgd5bDxXi45fSJ5DksKKhYR\nkaEmp8I3nojzeM3TvNe4gjEFo7hs2lewGA+9iS3tXdz33EosZiOXnD6RY6dXaFKViIgctJwJ30Qi\nwdNr/8pbde9Q5R7O5TO+itWUms0etjR0kEjA5+ZWc9yM9Gy+ICIiQ0fOzBL628aXWLLtP1S4yrhi\n5qU4zPaUvXdtSwCAymLNZhYRkUOXE+H78uYl/HPTYkocxVw18xvkWVIbknUtyVWqKnQrkYiIpEDW\nh+/r297iL+tfoNBWwHdmXkaBLT/l56hrCWA2GfAWpq43LSIiQ1dWh+/Suvd4Ys2zuC15fGfWZRQ7\nPCk/RyKRoK4lSJnHicmY1X9cIiIySGRtmizd9l/+sPpJHGYHV836BmVOb1rO4+vopisco6LYmZb3\nFxGRoScrZzuvavmY3374CFaThStmXMqwvIq0nauuNTneW67JViIikiJZ1/PtjoX5/co/YDQY+db0\nrzKqoCqt56tr3jHTWT1fERFJjazr+VqNFk4cfgxHjp5BqSF9Pd4demc6q+crIiIpknU9X4PBwFlj\nPsOU0vEDcr66lgAGoFw9XxERSZGsC9+BVtsSpLjAjs1y6OtDi4iIgMJ3vwJdEfyBsC45i4hISil8\n96Ouecd4ry45i4hI6ih896OuZ01nha+IiKSSwnc/NNNZRETSQeG7H727GWlDBRERSSGF737UtQRw\nOy3kOSyZLkVERHKIwncfwpEYzW1duuQsIiIpp/Ddh/rWIAm0rKSIiKSewncfNNlKRETSReG7D7rN\nSERE0kXhuw/q+YqISLoofPehriWAzWKiKN+W6VJERCTHKHz7EI8nqG8NUV7sxGAwZLocERHJMQrf\nPjS1h4jG4prpLCIiaaHw7cPODRU03isiIqmn8O3DzpnOCl8REUk9hW8fds501mVnERFJPYVvH+pa\nApiMBko9jkyXIiIiOUjhu4dEIkFtS5BSjwOzSX88IiKSekqXPbQHwoS6oxrvFRGRtFH47qGuWctK\niohIeil891DbM9mqUj1fERFJE4XvHnpvMypRz1dERNJD4buHHbcZlRcpfEVEJD0UvnuoawlQlG/D\nbjVnuhQREclRCt9dBLuitHWGNdNZRETSSuG7i7pWzXQWEZH0U/juYseGCprpLCIi6aTw3cXODRXU\n8xURkfRR+O6id0OFEvV8RUQkfRS+u6hrCeCym3E7LJkuRUREcpjCt0ckGqexLURFiQuDwZDpckRE\nJIcpfHs0+IIkElCp8V4REUkzhW+P3vFezXQWEZE0U/j22LmbkcJXRETSS+Hbo7bnNiNddhYRkXRT\n+PaoawliNRspKrBnuhQREclxCl8gnkhQ3xqkvMiJUTOdRUQkzRS+QEt7F5FoXItriIjIgFD4omUl\nRURkYCl8gVptqCAiIgNI4Yt6viIiMrDM/X1DIBBgwYIFtLe3E4lEuOKKK7j//vsJBoM4ncmwWrBg\nAVOnTk17selS1xLEaDBQVqTwFRGR9Os3fJ999llGjRrFNddcQ0NDAxdffDFer5dFixYxfvz4gagx\nrRKJBHUtAbweB2aTLgSIiEj69Zs2Ho+HtrY2APx+Px6PJ+1FDaSOYIRAV5QK9XpFRGSA9Bu+Z555\nJrW1tZx66qnMnz+fBQsWAHDPPfdw4YUXcv3119PV1ZX2QtOld7y3ROErIiIDo9/Lzs899xyVlZU8\n+OCD1NTUsHDhQi6//HImTJhAVVUVN9xwA3/84x+59NJL9/keHo8Ts9mU0sIBvF73Ib/Hu2ubAZhQ\nXZyS90uHwVpXqqmduUXtzC1qZ2r1G77Lly/nmGOOAWDixIk0NjZy0kknYTIlw/Skk07ihRde2O97\n+HzBFJS6O6/XTVNTxyG/z5pNrQDkWU0peb9US1U7Bzu1M7eonblF7Tz499uXfi87jxw5khUrVgCw\nfft2nE4nl156KX6/H4ClS5cybty4FJU68HSbkYiIDLR+e77nn38+CxcuZP78+USjUX7yk5/g8/m4\n5JJLcDgclJWVcdVVVw1ErWlR2xLE47bhsPX7RyEiIpIS/SaOy+Xi7rvv3uv4GWeckZaCBlKoO4qv\no5vJ1bk1g1tERAa3IX1ja31rciy6okjLSoqIyMAZ0uGr24xERCQThnj49vR8taGCiIgMoCEdvrXN\nyZ5vpWY6i4jIABrS4VvXEsRpM5Pvsma6FBERGUKGbPhGY3EafSEqSpwYDIZMlyMiIkPIkA3fBl+I\neCKh8V4RERlwQzZ867WylYiIZMiQDd9azXQWEZEMGbLhu+MeX810FhGRgTZ0w7c5iNlkpKTAkelS\nRERkiBmS4RtPJKhrDVBe5MRo1ExnEREZWEMyfFv9XYQjcSq1rKSIiGTAkAxfLSspIiKZNMTDVz1f\nEREZeEM0fHfc46uer4iIDLyhGb7NAQwGKC/STGcRERl4QzJ8a1uCeAscWMymTJciIiJD0JAL345g\nmM5QROO9IiKSMUMufHsnW5VovFdERDJjyIVvrTZUEBGRDBty4Vuve3xFRCTDhlz41mpDBRERybAh\nF751zUEKXFacdkumSxERkSFqSIVvdzhGi79L470iIpJRQyp861s101lERDJvSIXvzvFeha+IiGTO\nkArfOt1mJCIig8AQC1/dZiQiIpk35MLXbjVRmGfNdCkiIjKEDZnwjcXjNLQGqSh2YTAYMl2OiIgM\nYUMmfBt9IWLxhBbXEBGRjBsy4asNFUREZLAYQuGrmc4iIjI4DJnwrW1O9nx1j6+IiGTakAnf+tYA\nZpOBkkJ7pksREZEhbkiEbyKRoK4lSJnHick4JJosIiKD2JBIIl9HN13hmMZ7RURkUBgS4auVrURE\nZDAZEuG7Y0OFihL1fEVEJPOGRPju6PlqprOIiAwGQyN8mwMYgLIi9XxFRCTzhkb4tgYpLrBjs5gy\nXYqIiEjuh2+gK4I/ENZkKxERGTRyPnzrmnfMdNYlZxERGRxyPnx3zHSu1IYKIiIySOR8+GpDBRER\nGWyGQPhqgQ0RERlccj58a5sDuJ0W8hyWTJciIiIC5Hj4hiMxWtq71OsVEZFBJafDt741SAKo1Hiv\niIgMIjkdvhrvFRGRwSjHw1cbKoiIyOCT0+Fbqw0VRERkEMrp8K1rCWCzmPC4bZkuRUREpFfOhm8s\nHqehNUh5sRODwZDpckRERHrlbPg2t3cRjSU001lERAadnA3fnRsqaLxXREQGF3N/3xAIBFiwYAHt\n7e1EIhGuuOIKvF4vN954IwATJkzgJz/5Sbrr/MR2rums8BURkcGl3/B99tlnGTVqFNdccw0NDQ1c\nfPHFeL1eFi5cyPTp07nmmmt47bXXOP744wei3gO2czcjXXYWEZHBpd/Lzh6Ph7a2NgD8fj+FhYVs\n376d6dOnA3DiiSfy1ltvpbfKg1DXEsRkNOAtdGS6FBERkd302/M988wzeeaZZzj11FPx+/3cd999\n3HTTTb3PFxcX09TUtN/38HicmM2mQ692D16vu8/jiUSC+tYglV4XFeUFKT/vQNtXO3ON2plb1M7c\nonamVr/h+9xzz1FZWcmDDz5ITU0NV1xxBW73zuISiUS/J/H5godWZR+8XjdNTR19n6+jm2BXlIlV\njn1+T7bYXztzidqZW9TO3KJ2Hvz77Uu/4bt8+XKOOeYYACZOnEh3dzfRaLT3+YaGBkpLS1NQZurU\n90620niviIgMPv2O+Y4cOZIVK1YAsH37dlwuF2PGjOHdd98F4KWXXuLYY49Nb5WfkJaVFBGRwazf\nnu/555/PwoULmT9/PtFolBtvvBGv18v1119PPB5nxowZzJ07dyBqPWDaUEFERAazfsPX5XJx9913\n73X88ccfT0tBqdC7lWCRer4iIjL45OQKV7UtAYrzbdisqZ9hLSIicqhyLnyDXVHaO8Na2UpERAat\nnAvfHeO95ZrpLCIig1QOhq9mOouIyOCWg+Gre3xFRGRwy8Hw7ZnpXKKer4iIDE45F761LQHyHBby\nndZMlyIiItKnnArfSDRGU1tIl5xFRGRQy6nwbWgNkUig24xERGRQy6nwrdVkKxERyQI5Fb71OyZb\nqecrIiKDWE6F746eb6V6viIiMojlVPjWtQSxWowUFdgzXYqIiMg+5Uz4xuMJ6luDlBc5MRoMmS5H\nRERkn3ImfJv9XUSicS0rKSIig17OhG9ds2Y6i4hIdsid8NVMZxERyRI5FL7q+YqISHbIofANYjQY\nKCtS+IqIyOCWE+GbSCSoawng9Tgwm3KiSSIiksNyIqn8wQiBrqgW1xARkayQE+G7c6azJluJiMjg\nlxvhq8lWIiKSRXIifGt1m5GIiGSRnAjfevV8RUQki+RE+Na2BPG4bThs5kyXIiIi0q+sD99QdxRf\nR7d6vSIikjWyPnzrWzXeKyIi2SXrw7e25zYj3eMrIiLZIuvDVxsqiIhItsmB8NVMZxERyS45EL5B\nnDYz+S5rpksRERE5IFkdvtFYnEZfiIoSJwaDIdPliIiIHJCsDt8GX4h4IqHxXhERySpZHb51vTOd\nFb4iIpI9sjt8NdlKRESyUJaHb89tRiXq+YqISPbI6vCtbQlgNhkpybdnuhQREZEDlrXhG48nqG8N\nUl7kxGjUTGcREckeWRu+zW0hwpE4lSUa7xURkeySteG7tbED0LKSIiKSfbI3fBs6Ac10FhGR7JO1\n4butp+ere3xFRCTbZG34bm3owGCAsiJHpksRERH5RLI4fDvxFjiwmE2ZLkVEROQTycrw7QiG6QiG\nNd4rIiJZKSvDVytbiYhINsvK8K3Vms4iIpLFsjJ865qTPV/NdBYRkWyUneHb2/NV+IqISPbJ2vAt\nyrfhtJszXYqIiMgnlnXh2xWO0uLvZnipO9OliIiIHJSsC18DBlx2MzPHezNdioiIyEHJuuu2NquJ\nX151DBXlBTQ1dWS6HBERkU8s63q+AGZTVpYtIiICZGn4ioiIZDOFr4iIyADrd8z3//7v/3j++ed7\nv165ciVTp04lGAzidCZXmFqwYAFTp05NX5UiIiI5pN/wPffcczn33HMBWLZsGf/4xz9Yt24dixYt\nYvz48WkvUEREJNd8osvOv/71r/n2t7+drlpERESGhAO+1eiDDz6goqICrzd5f+0999yDz+djzJgx\nLFy4ELvdvs/XejxOzGnYd9frHRoLbaiduUXtzC1qZ24ZqHYecPg+9dRTfPGLXwTgK1/5ChMmTKCq\nqoobbriBP/7xj1x66aX7fK3PFzz0Svfg9bqHxH2+amduUTtzi9qZW1Ldzv0F+QFfdl66dCmzZs0C\n4NRTT6WqqgqAk046iTVr1hxiiSIiIkPHAYVvQ0MDLpcLq9VKIpHgkksuwe/3A8lQHjduXFqLFBER\nySUHdNm5qamJoqIiAAwGA+eddx6XXHIJDoeDsrIyrrrqqrQWKSIikksOKHynTp3KAw880Pv1GWec\nwRlnnJG2okRERHKZIZFIJDJdhIiIyFCi5SVFREQGmMJXRERkgCl8RUREBpjCV0REZIApfEVERAaY\nwldERGSAZV34/uxnP+P888/nggsu4IMPPsh0OWlz2223cf7553POOefw0ksvZbqctOrq6uKUU07h\nmWeeyXQpafP8889z1llncfbZZ7NkyZJMl5MWgUCAK6+8kosuuogLLriAN954I9MlpdyaNWs45ZRT\neOyxxwCoq6vjoosuYt68eVx99dWEw+EMV5gafbXzkksuYf78+VxyySU0NTVluMLU2LOdO7zxxhtM\nmDAhrefOqvBdtmwZmzdv5oknnuCWW27hlltuyXRJafH222+zdu1annjiCR544AF+9rOfZbqktLrv\nvvsoKCjIdBlp4/P5+PWvf83jjz/Ob3/7WxYvXpzpktLi2WefZdSoUfzhD3/g7rvvzrl/n8FgkJtv\nvpmjjjqq99g999zDvHnzePzxxxk5ciRPPfVUBitMjb7aedddd3Heeefx2GOPceqpp/Lwww9nsMLU\n6KudAN3d3dx///29O/ilS1aF71tvvcUpp5wCwJgxY2hvb6ezszPDVaXe7NmzufvuuwHIz88nFAoR\ni8UyXFV6rF+/nnXr1nHCCSdkupS0eeuttzjqqKPIy8ujtLSUm2++OdMlpYXH46GtrQ0Av9+Px+PJ\ncEWpZbVa+f3vf09paWnvsaVLl3LyyScDcOKJJ/LWW29lqryU6audN9xwA6eddhqw+885m/XVToDf\n/va3zJs3D6vVmtbzZ1X4Njc37/YPuqioKGcuf+zKZDLhdDqB5FaOxx13HCZT6vdDHgxuvfVWrrvu\nukyXkVbbtm2jq6uLb33rW8ybNy8nfkH35cwzz6S2tpZTTz2V+fPns2DBgkyXlFJms3mvfctDoVDv\nL+ni4uKc+H3UVzudTicmk4lYLMbjjz/O5z73uQxVlzp9tXPjxo3U1NRw+umnp//8aT9DGuX6ypiv\nvPIKTz31FA899FCmS0mLv/zlL8ycOZMRI0ZkupS0a2tr495776W2tpavfOUr/Otf/8JgMGS6rJR6\n7rnnqKys5MEHH6SmpoaFCxfm9Dj+nnL991EsFuPaa6/lyCOP3OtSba5YtGgRP/rRjwbkXFkVvqWl\npTQ3N/d+3djYmPbr8pnyxhtv8Nvf/pYHHngAt3vfGzJnsyVLlrB161aWLFlCfX09VquV8vJy5s6d\nm+nSUqq4uJhZs2ZhNpupqqrC5XLR2tpKcXFxpktLqeXLl3PMMccAMHHiRBobG4nFYjl71QaSPcKu\nri7sdjsNDQ17XcLMJT/4wQ8YOXIkV155ZaZLSYuGhgY2bNjA//7v/wLJfJk/f/5ek7FSJasuOx99\n9NG8+OKLAHz00UeUlpaSl5eX4apSr6Ojg9tuu43f/e53FBYWZrqctLnrrrt4+umnefLJJzn33HP5\n9re/nXPBC3DMMcfw9ttvE4/H8fl8BIPBnBsPBRg5ciQrVqwAYPv27bhcrpwOXoC5c+f2/k566aWX\nOPbYYzNcUXo8//zzWCwWvvOd72S6lLQpKyvjlVde4cknn+TJJ5+ktLQ0bcELWdbzPeyww5gyZQoX\nXHABBoOBG264IdMlpcULL7yAz+fjf/7nf3qP3XrrrVRWVmawKjlYZWVlnHbaaZx33nkA/OhHP8Jo\nzKr/9x6Q888/n4ULFzJ//nyi0Sg33nhjpktKqZUrV3Lrrbeyfft2zGYzL774IrfffjvXXXcdTzzx\nBJWVlXzhC1/IdJmHrK92trS0YLPZuOiii4DkhNds//n21c5f/epXA9bh0ZaCIiIiAyz3/vstIiIy\nyCl8RUREBpjCV0REZIApfEVERAaYwldERGSAKXxFREQGmMJXRERkgCl8RUREBtj/B4ZvM1rzQGIl\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb41d932b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vEsatUk1EmTp",
        "colab_type": "code",
        "outputId": "6697b0d7-ec97-422b-b2a8-7479bb320f7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        }
      },
      "cell_type": "code",
      "source": [
        "!pip3 install pillow==4.0.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pillow==4.0.0 in /usr/local/lib/python3.6/dist-packages (4.0.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow==4.0.0) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Bdd5aONjGO5_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import PIL.ImageOps"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gUPLBiV0gDd8",
        "colab_type": "code",
        "outputId": "8b38d0a7-1791-4ef2-8994-dc9ea07e2aa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "\n",
        "url = 'https://images.homedepot-static.com/productImages/007164ea-d47e-4f66-8d8c-fd9f621984a2/svn/architectural-mailboxes-house-letters-numbers-3585b-5-64_1000.jpg'\n",
        "response = requests.get(url, stream = True)\n",
        "img = Image.open(response.raw)\n",
        "plt.imshow(img)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcb456210f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vSVEmkjEJ8UMPPYRZs2Zh991393y/nK+okyv1AyPUn//85+N+7HXr1o37MRWl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\nDDqZYAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb451eea90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "v24yJujOERx8",
        "colab_type": "code",
        "outputId": "f3c6a6ed-ca57-4e42-8e3b-aa3405b59bdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "cell_type": "code",
      "source": [
        "img = PIL.ImageOps.invert(img)\n",
        "img = img.convert('1')\n",
        "img = transform(img) \n",
        "plt.imshow(im_convert(img))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcb41ce5c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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woQ4fPqzJyUmVlpbq9OnTCoVCTi9zVv49U319vQYGBlRcXCxJ2rVrl9avX+/sImepublZ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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb425f13c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Gd_XSROHFzsd",
        "colab_type": "code",
        "outputId": "534f000c-1119-4fda-9021-ff8843280f6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "img = img.view(img.shape[0], -1)\n",
        "output = model(img)\n",
        "_, pred = torch.max(output, 1)\n",
        "print(pred.item())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UE7haDIMG2cE",
        "colab_type": "code",
        "outputId": "14ae08d1-b70f-4b6b-b47d-e631ab8a203f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "cell_type": "code",
      "source": [
        "dataiter = iter(validation_loader)\n",
        "images, labels = dataiter.next()\n",
        "images_ = images.view(images.shape[0], -1)\n",
        "output = model(images_)\n",
        "_, preds = torch.max(output, 1)\n",
        "\n",
        "fig = plt.figure(figsize=(25, 4))\n",
        "\n",
        "for idx in np.arange(20):\n",
        "  ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])\n",
        "  plt.imshow(im_convert(images[idx]))\n",
        "  ax.set_title(\"{} ({})\".format(str(preds[idx].item()), str(labels[idx].item())), color=(\"green\" if preds[idx]==labels[idx] else \"red\"))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fcb41ce5240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "nzLUhaxBIHwB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
